RAG Chatbot Development Canada 2026
AI chatbots that answer from your business knowledge - not generic training data.
Custom RAG chatbots powered by GPT-4o, deployed on your website, WhatsApp or Slack and trained on your documentation, FAQs and product information.
What you get
Full AI chatbot build.
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Knowledge base ingestion - Your documents, website content, FAQs and PDFs processed, chunked and indexed into a vector database.
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RAG pipeline development - Retrieval logic built and tuned to surface the most relevant knowledge chunks for each user query before LLM response generation.
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GPT-4o or Claude integration - LLM API integrated with system prompts engineered for your brand tone, response format and scope boundaries.
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Chat UI development - Custom branded chat widget or in-app chat interface built to your design specifications.
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Multi-channel deployment - Website widget, WhatsApp Business API, Slack, Teams or custom app - configured for your preferred channels.
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Human handoff integration - Seamless escalation to human agents via Intercom, Zendesk, Gorgias or email when the chatbot cannot confidently answer.
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Analytics and monitoring - Query volume, resolution rate, handoff rate, confidence scores and topic clustering to identify knowledge gaps.
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Testing and accuracy tuning - 200+ test queries run before launch to verify accuracy and identify knowledge gaps for remediation.
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What to expect
Week 1: Knowledge base collection and RAG architecture. Week 2: Build and initial testing. Week 3: Accuracy tuning and UI development. Week 4: Deployment and monitoring setup. Most chatbots answer 80%+ of queries correctly from day one.
"A chatbot that confidently gives wrong answers is worse than no chatbot at all. We tune for accuracy before we tune for anything else."
Our process
How we build your AI chatbot.
1
Week 1
Knowledge base and architecture
Collect all source documents, define chatbot scope and tone, select vector database and chunking strategy. Architecture review ensures the RAG pipeline will retrieve accurately before any code is written.
2
Week 2
Build and initial testing
RAG pipeline built, LLM integrated and system prompts engineered. First working demo delivered for your review. 50 test queries run to identify retrieval gaps and prompt refinement needs.
3
Week 3
Accuracy tuning and UI
Retrieval parameters tuned, knowledge gaps filled and prompt engineering refined based on test results. Chat UI built and customized to your brand. Human handoff integration configured and tested.
4
Week 4
Deployment and monitoring
Production deployment on your chosen channels. Analytics dashboard configured, error monitoring set up and knowledge base update process documented. 30-day post-launch tuning support included.
Chatbot results
What our AI chatbots achieve.
0%
Avg. query resolution rate without human handoff
0 min
Avg. response time vs 6 hr manual support average
0%
Avg. reduction in human support ticket volume
FAQ
AI chatbot development questions answered.
A RAG (Retrieval-Augmented Generation) chatbot retrieves relevant information from your specific knowledge base before generating a response. Unlike a standard GPT chatbot that draws only from its training data, a RAG chatbot answers questions using your exact product documentation, FAQs, policies and business-specific information - making it far more accurate for business use.
We deploy chatbots as website widgets (floating chat bubble on your site), WhatsApp Business API bots, Slack or Teams bots, mobile app chat interfaces and custom web app chat components. Multi-channel deployment (website + WhatsApp simultaneously) is available.
Any text-based knowledge: website content, product documentation, PDF manuals, FAQ pages, support ticket history, Google Docs, Notion pages, Confluence wikis and custom databases. We ingest and index your knowledge base into a vector store so the chatbot retrieves the most relevant sections before answering.
A well-built RAG chatbot on a quality knowledge base typically answers 85-95% of queries correctly. Accuracy depends on knowledge base quality, query clarity and how well edge cases are handled. We include a testing and refinement phase before deployment and ongoing monitoring to catch and fix accuracy issues.
Yes. We build human handoff flows triggered by keywords, sentiment detection, user requests or conversation complexity thresholds. Handoffs connect to your existing support tools: Intercom, Gorgias, Zendesk, Freshdesk or a simple email notification.
We set up either automatic re-indexing (when source documents change, the knowledge base updates automatically) or a simple admin interface where your team can upload new documents and trigger a re-index. Monthly knowledge base audits are available as a managed service.